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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat
Afridiansyah, Rahmanda
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Computer Science & IT Economics, Econometrics & Finance Health Professions Industrial & Manufacturing Engineering Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Comparison of EfficientNetB1 Model Effectiveness in Identifying Fish Diseases in South Asian Fish Diseases and Salmon Fish Diseases Afridiansyah, Rahmanda; Setiadi, De Rosal Ignatius Moses
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8677

Abstract

The purpose of this study is to evaluate the effectiveness of the EfficientNetB1 model in identifying fish diseases across two distinct datasets: South Asian Fish Diseases and Salmon Fish Diseases. The South Asian Fish Diseases dataset includes seven categories: red bacterial disease, aeromoniasis, gill bacterial disease, fungal saprolegniasis, parasitic disease, and white tail viral disease. The Salmon dataset is divided into two parts: FreshFish and InfectedFish. Using the EfficientNetB1 algorithm, each dataset was separately trained and tested to predict species and disease. Results showed an accuracy of 98.14% for the South Asian Fish Diseases dataset and 99.18% for the Salmon Diseases dataset. These findings support the argument that the model possesses sufficient capability to detect diseases affecting various fish species. This suggests that the model could be a valuable tool in the aquaculture industry for disease management and detection strategies.
Co-Authors Candra, Rejka Aditya De Rosal Ignatius Moses Setiadi Junta Zeniarja Khoirunnisa, Emila Manurung, Ayub Michaelangelo Rama Eka Saputra Ramadhan, Ahnaf Irfan Rezaroebojo, Rizal Rohman, Adib Annur